AI and ML-Based secure employee data management model using FBCSR and HSMPC with blockchain for privacy preservation and insider threat detection in HRM

The purpose of this study is to address the critical challenge of insider threats and privacy risks in Human Resource Management (HRM) employee data systems by developing a secure and scalable framework for authorized data access and threat detection. The proposed methodology integrates Artificial Intelligence (AI), Machine Learning (ML), and blockchain technologies, where redundant employee records are removed, sensitive information is extracted using the Fuzzy Basis Cubic Spline Rule (FBCSR), and privacy is preserved through the Rössler Attractor K-Anonymity (RAKA) approach. In the unified workflow, RAKA first anonymizes sensitive employee attributes, HSMPC then encrypts and securely stores the protected data on the blockchain for authorized sharing, and finally FBCSR enables real-time insider threat detection from user access activities, providing an end-to-end security solution for HRM applications. Employee data are further partitioned using Fuzzy C-Means (FCM) clustering and securely encrypted via Hybrid Secure Multi-Party Computation (HSMPC) before being stored on a blockchain supported by the PoSABS consensus mechanism and context-aware smart contract access control. Experiments conducted using the Employee dataset and the EventSim dataset demonstrate that the proposed framework achieves an insider threat detection accuracy of 96.19% while effectively preventing unauthorized access with improved scalability and encryption efficiency. The study provides an important implication for HRM organizations and policy makers by recommending blockchain-enabled privacy-preserving access governance to strengthen employee data protection and insider threat monitoring in real-world enterprise environments. The originality of this research lies in the novel integration of FBCSR-based threat detection with HSMPC encryption and scalable PoSABS blockchain consensus, offering a robust contribution toward secure employee data management in modern HRM systems.